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- W4286906971 abstract "Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous cars, driver assistance systems, and in-vehicle entertainment, many vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources? To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles' sensing capabilities. Our main contribution is a detailed mathematical specification for a task-based, distributed service placement model that scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to an autonomous vehicular edge computing-based na{i}ve solution." @default.
- W4286906971 created "2022-07-25" @default.
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- W4286906971 date "2021-10-11" @default.
- W4286906971 modified "2023-09-24" @default.
- W4286906971 title "Scaling and Placing Distributed Services on Vehicle Clusters in Urban Environments" @default.
- W4286906971 doi "https://doi.org/10.48550/arxiv.2110.09471" @default.
- W4286906971 hasPublicationYear "2021" @default.
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